Unlike Derek Zoolander, ants don’t have any difficulty turning left. New research from the University of Bristol has now found rock ants often have one eye slightly better than the other, which could help explain why most of them prefer to turn left, given the choice.

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For more than half a century, U.S. government officials have considered disaster scenarios, such as the consequences of a nuclear bomb going off in Washington, D.C. Only now, instead of following fixed story lines and predictions assembled ahead of time, they are using computers to play what-if with an entire artificial society: an advanced type of computer simulation called an agent-based model. Today’s version of the nuclear attack model includes a digital simulation of every building in the area affected by the bomb, as well as every road, power line, hospital, and even cell tower. The model includes weather data to simulate the fallout plume. And the scenario is peopled with some 730,000 agents. Each agent is an autonomous subroutine that responds in reasonably human ways to other agents and the evolving disaster by switching among multiple modes of behavior. The point of such models is to avoid describing human affairs from the top down with fixed equations, as is traditionally done in such fields as economics and epidemiology. Instead, outcomes such as a financial crash or the spread of a disease emerge from the bottom up, through the interactions of many individuals, leading to a real-world richness and spontaneity that is hard to simulate otherwise. The models tend to be big, computation-wise—forcing the agents to be relatively simple-minded. But computers keep getting bigger and more powerful, as do the data sets used to populate and calibrate the models. In fields as diverse as economics, transportation, public health, and urban planning, more and more decision-makers are taking agent-based models seriously.

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The vast majority of the nearly half-million infants born prematurely in the United States are given antibiotics, even without evidence of infection. Many preemies are kept on the drugs after blood tests say they are not sick. Yet that practice, once considered the best way to protect a hospital’s most vulnerable patients, is now being challenged. Some studies suggest that even while helping fight certain infections, those drugs may encourage others by wiping out an infant’s developing gut microbiome. Disrupting that microbial ecosystem may also promote a host of problems later in life, such as asthma and obesity. And recent research indicates that long after preemies leave the neonatal intensive care unit, they can harbor many antibiotic-resistant microorganisms, potentially endangering not only themselves, but also the wider population.

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If you walked the cobblestone streets and bustling markets of 16th and 17th century Mexico City, you would see people born all over the world: Spanish settlers, indigenous Americans, Africans, and Asians. All these populations met and mingled for the first time in colonial Latin America. Historical documents describe this cultural mixture, but now, international teams of researchers are enriching our view of colonial Latin America by analyzing the genomes of today’s people. Aided by sophisticated statistics and worldwide genetic databases, they can tease apart ancestry and population mixing with more nuance than ever before. The results, reported at a meeting this week and in a preprint, tell stories that have been largely forgotten or were never recorded in historical documents. From the immigration of enslaved Filipinos and Africans to that of formerly Jewish families forbidden to travel to the colonies, hidden histories are emerging.

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Societal unrest and similar events are important for societies, but it is often difficult to quantify their effects on individuals, hindering a timely and effective policy-making in emergencies and in particular localized social shocks such as protests. Traditionally, effects are assessed through economic indicators or surveys with relatively low temporal and spatial resolutions. In this work, we compute two behavioral indexes, based on the use of credit card transaction data, for measuring the economic effects of a series of protests on consumer actions and personal consumption. Using data from a metropolitan area in an OECD country, we show that protests affect consumers’ shopping frequency and spending, but in noticeably different ways. The effects show strong temporal and spatial patterns, vary between neighborhoods and customers of different socio-demographical characteristics as well as between merchants of different categories, and suggest interesting subtleties in purchase behavior such as displaced or delayed shopping activities. Our method can generally serve for the real-time monitoring of the effects of major social shocks or events on urban economy and consumer sentiment, providing high-resolution and cost-effective measurement tools to complement traditional economic indicators.

Methods for quantifying effects of social unrest using credit card transaction data